This repository contains the source code and additional resources discussed in our CharacterGPT video lecture. The focus of the lecture was on the basic understanding and implementation of the CharacterGPT model, an educational tool designed to demonstrate the principles behind GPT-style language models.
The lecture did not extensively cover the aspect of model initialization, which is critical for achieving optimal performance. The initial weights setup, as implemented in the current codebase, leads to slower convergence due to starting at a suboptimal point in the weight space.
For a better initialization approach, please refer to the official CharacterGPT model.py under the # init all weights comment, which details the usage of the _init_weights function. Unfortunately, due to differences in naming and module storage conventions in this repository, it's not feasible to directly transfer the exact code snippets without adjustments.
Stay Tuned Stay connected for the upcoming video and corresponding code updates. Thank you for your interest and patience!
The implementation emphasizes the importance of proper weight initialization for optimal neural network performance. Refer to the model.py for insights on the _init_weights function, pivotal for accelerating convergence.
Due to unique naming and storage of modules in this repository, adaptations from the original CharacterGPT structure are noted, preventing direct code transfers. Future updates will align closer to best practices and improve on current limitations.